DID YOU KNOW?
• Use of biomarkers in clinical studies rose 15% between 2010 and 2015, increasing from 43% of trials measuring biomarkers to 58%.
• The Integrity database from Clarivate Analytics includes a unique biomarker knowledge store that connects experimental research, drug R&D and clinical studies for pivotal insights and decision making.
• The Biomarkers Module allows you to verify quickly if any potential biomarkers you identify in MetaCore using Omics data analysis are already known in the literature, and if so, learn how and where they have been used.
In this webinar, we will discuss using the Biomarkers Module of Integrity to retrieve a list of prognostic markers of prostate cancer that have translated successfully to the clinic. We will upload the list of biomarkers into MetaCore for further analysis and associate the results with public data for genes that are differentially expressed in recurrent prostate cancer compared to non-recurrent (available in the GEO series entry GSE25136), to answer these questions:
• Which prostate cancer-specific pathways are statistically enriched with the curated prognostic markers?
• Does analysis of the public gene expression data highlight any potential new biomarkers of prostate cancer prognosis?
• What curated evidence is available to support our hypothesis?
Cancer immunotherapies importance as an integral standard of care across oncological indications continues to grow. Antibody inhibition of CTLA-4 and PD-1 enhances the antitumor immune response (1), yielding high rates of objective clinical responses and ultimately melanoma and lung cancer FDA approvals. A rising challenge for these therapies is the resistance to treatment in a subset of patients due to acquired or intrinsic mechanisms (2). Beyond mutations in the tumor cells themselves, the tumor microenvironment can play an important role in the response to these treatments. It was recently shown that when treating melanoma patients with Ipilimumab, myeloid derived suppressor cells (MDSC) infiltrate into tumor cells of resistant patients and could be a predictive biomarker for resistance(3).
We will be using MetaCore and the Data Annotation & Processing tool to calculate the differentially expressed genes in a publicly available microarray dataset and upload the results into MetaCore for analysis. The data used in this session was reported in the Gene Expression Omnibus (GEO) dataset GSE41620 which studied MDSCs taken from naïve mouse blood and from mice injected with the lymphoma RMA-S cell line. Samples were drawn from blood and tumors of the xenograph and naïve mice. Using this data Pathway Map Creator in MetaCore to answer these questions:
• How to calculate differentially expressed genes from a GEO dataset and upload this data into Metacore?
• What pathway maps are potentially disrupted by the differentially expressed genes?
• What transcription factors could be regulating a significant number of the genes?